15,358 research outputs found
Ways of Applying Artificial Intelligence in Software Engineering
As Artificial Intelligence (AI) techniques have become more powerful and
easier to use they are increasingly deployed as key components of modern
software systems. While this enables new functionality and often allows better
adaptation to user needs it also creates additional problems for software
engineers and exposes companies to new risks. Some work has been done to better
understand the interaction between Software Engineering and AI but we lack
methods to classify ways of applying AI in software systems and to analyse and
understand the risks this poses. Only by doing so can we devise tools and
solutions to help mitigate them. This paper presents the AI in SE Application
Levels (AI-SEAL) taxonomy that categorises applications according to their
point of AI application, the type of AI technology used and the automation
level allowed. We show the usefulness of this taxonomy by classifying 15 papers
from previous editions of the RAISE workshop. Results show that the taxonomy
allows classification of distinct AI applications and provides insights
concerning the risks associated with them. We argue that this will be important
for companies in deciding how to apply AI in their software applications and to
create strategies for its use
An Exploratory Study of Field Failures
Field failures, that is, failures caused by faults that escape the testing
phase leading to failures in the field, are unavoidable. Improving verification
and validation activities before deployment can identify and timely remove many
but not all faults, and users may still experience a number of annoying
problems while using their software systems. This paper investigates the nature
of field failures, to understand to what extent further improving in-house
verification and validation activities can reduce the number of failures in the
field, and frames the need of new approaches that operate in the field. We
report the results of the analysis of the bug reports of five applications
belonging to three different ecosystems, propose a taxonomy of field failures,
and discuss the reasons why failures belonging to the identified classes cannot
be detected at design time but shall be addressed at runtime. We observe that
many faults (70%) are intrinsically hard to detect at design-time
An Exploratory Study of Field Failures
Field failures, that is, failures caused by faults that escape the testing
phase leading to failures in the field, are unavoidable. Improving verification
and validation activities before deployment can identify and timely remove many
but not all faults, and users may still experience a number of annoying
problems while using their software systems. This paper investigates the nature
of field failures, to understand to what extent further improving in-house
verification and validation activities can reduce the number of failures in the
field, and frames the need of new approaches that operate in the field. We
report the results of the analysis of the bug reports of five applications
belonging to three different ecosystems, propose a taxonomy of field failures,
and discuss the reasons why failures belonging to the identified classes cannot
be detected at design time but shall be addressed at runtime. We observe that
many faults (70%) are intrinsically hard to detect at design-time
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Predicting Software Defects Based on Cognitive Error Theories
As the primary cause of software defects, human error is the key to understanding and perhaps to predicting and preventing software defects. However, little research has been done to forecast software defects based on defects' cognitive nature. This paper proposes an idea for predicting software defects by applying the current scientific understanding of human error mechanisms. This new prediction method is based on the main causal mechanism underlying software defects: an error-prone scenario triggers a sequence of human error modes. Preliminary evidence for supporting this idea is presented
Towards Structural Testing of Superconductor Electronics
Many of the semiconductor technologies are already\ud
facing limitations while new-generation data and\ud
telecommunication systems are implemented. Although in\ud
its infancy, superconductor electronics (SCE) is capable of\ud
handling some of these high-end tasks. We have started a\ud
defect-oriented test methodology for SCE, so that reliable\ud
systems can be implemented in this technology. In this\ud
paper, the details of the study on the Rapid Single-Flux\ud
Quantum (RSFQ) process are presented. We present\ud
common defects in the SCE processes and corresponding\ud
test methodologies to detect them. The (measurement)\ud
results prove that we are able to detect possible random\ud
defects for statistical purposes in yield analysis. This\ud
paper also presents possible test methodologies for RSFQ\ud
circuits based on defect oriented testing (DOT)
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